Gamified Simulation for Onboarding Health Care Teams in Emergency Care: Development and Preliminary Feasibility Study
Bibliographic record
Abstract
Background: High staff turnover is a widespread issue across nearly all hospital departments, often exceeding 20% annually. This constant flux disrupts continuity of care and creates a recurring challenge: how to rapidly integrate new employees into complex clinical environments, both physically and functionally. Traditional onboarding methods struggle to meet this demand, particularly in services operating 24/7, such as emergency departments (EDs). Objective: This formative study presents the design and implementation of a web-based 3D gamified simulation platform aimed at improving staff onboarding in clinical environments. The paper outlines both the technical architecture-with guidance for hospital IT departments-and the acceptability and usability for permanent staff, who play a key role in ensuring onboarding continuity. We sought to assess whether such a tool could be autonomously managed and well received by health care professionals. Methods: The intervention consisted of 2 linked components: a real-time, browser-based 3D simulation replicating the hospital's ED and a web-based quest editor allowing nontechnical staff to update training content. The system supports self-paced onboarding through location-based tasks, object searches, quizzes, and simulated staff interactions. Two preliminary usability studies were conducted: one with 37 ED staff members testing the 3D simulation and another with 9 users exploring the quest editor. Feedback was gathered through anonymous questionnaires and a descriptive analysis. Results: Early results showed high feasibility and acceptability. Among 3D simulation testers (n=37), 90% (33/37) found the tool helpful for understanding the department's structure, and 81% (30/37) believed it would be useful for new staff. The inclusion of personal anecdotes and gamified tasks was viewed as engaging and motivating. The quest editor (n=9) was positively rated by 91% (8/9) of users, who appreciated the ability to autonomously update content without IT support. These findings support the dual promise of the platform (ie, pedagogical flexibility and technical sustainability). Conclusions: This work demonstrates the feasibility of a gamified simulation platform designed for high-turnover clinical environments. It highlights both the operational deployment framework and the early acceptability among key staff members. While further validation with actual new hires is needed, this formative study shows promising potential for generalization beyond emergency care. The modular and editable nature of the system makes it a viable solution for scalable onboarding in other hospital departments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".